Autonomous-driving systems are built in different ways, but no one architecture has been shown to be universally best. The main approaches are modular pipelines, end-to-end learning, and hybrids that combine elements of both. These are engineering choices; they are separate from SAE automation levels, which describe whether the human or the system is responsible for driving and responding to limits.
First, separate the system’s architecture from its automation level
“Modular,” “end-to-end,” and “hybrid” describe how engineers organize a driving system’s software. SAE levels describe the division of driving responsibility between a person and an automated system. One label does not tell you the other: an architecture alone does not establish what a vehicle can do, where it can do it, or whether a person must supervise it.
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Under the SAE J3016 taxonomy, Level 0 through Level 5 classify driving automation by the system’s role in the driving task and fallback. The levels are not a score of software quality or capability in every situation.
For drivers in the United States, NHTSA says Levels 0–2 require the driver to remain engaged and monitor the driving environment. A Level 2 system may support steering and speed at the same time, but the human remains responsible. NHTSA’s current driver-assistance guidance, accessed in October 2026, says Level 3–5 technologies are not available on vehicles for consumer purchase. Its automated-vehicle safety page describes public-road testing and pilots as limited to designated locations and conditions. These are U.S.-specific statements, not a description of availability or regulation everywhere.
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NHTSA cautions that “self-driving” can mislead people about how they need to interact with a vehicle. Do not stop supervising a driver-assistance feature because of its marketing name or because you have read about its software architecture; follow the vehicle’s instructions and applicable law.
How does a modular driving system work?
A modular pipeline divides driving into stages, commonly including perception, prediction, planning, and control. Perception estimates what is around the vehicle; prediction estimates how other road users may move; planning selects a path or maneuver; and control translates that choice into steering, braking, or acceleration.
The stages create explicit interfaces that teams can inspect and change separately. For example, the CARLA simulator paper (2017) describes a research system with vision-based perception, a rule-based planner, and a maneuver controller. That is an example, not a definition of every modular system.
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A visible pipeline can make it easier to trace which stage contributed to a decision or failure. But an error in one stage can affect later stages, and decomposition by itself does not prove a system is safer or less capable. Performance still depends on the quality of the components, their interfaces, and the system’s testing.
What does end-to-end autonomous driving mean?
An end-to-end approach learns a more direct mapping from sensor inputs to driving commands or a motion plan. Rather than relying entirely on separately designed interfaces between perception and planning, a model can learn features jointly across parts of the driving task.
The 2023 survey End-to-end Autonomous Driving: Challenges and Frontiers reviews more than 270 papers, according to the survey authors. It discusses joint optimization as a potential advantage, while identifying interpretability, robustness, and causal confusion as challenges. CARLA’s 2017 paper also compares end-to-end models trained through imitation learning and reinforcement learning.
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“End-to-end” does not necessarily mean that a production vehicle consists of one neural network with no supporting software, rules, monitoring, or safety controls. It describes a learned mapping across some portion of the driving task; implementations can still include other system components.
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Why combine modular and end-to-end methods?
Hybrid systems occupy a spectrum rather than a single standardized category. A team might use learned models for perception or prediction while retaining explicit planning structure, constraints, monitors, or fallback behavior. Other combinations are possible; the label alone does not tell you which functions are learned or how the system responds when a model is uncertain.
This design can let developers use learning where data-driven pattern recognition is useful while keeping some decisions or limits explicit. It also adds integration work: the learned and structured parts must exchange information reliably, and monitors and fallback paths must be validated as part of the whole system. The available evidence does not establish hybrid systems as a benchmark winner.
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How do the approaches compare?
The trade-offs below are tendencies, not guarantees. Implementations vary, and no apples-to-apples independent benchmark in the cited sources ranks these architecture families across safety or performance.
| Question | Modular pipeline | End-to-end learning | Hybrid system |
|---|---|---|---|
| Can a team trace a decision? | Separate stages and interfaces can make it easier to inspect where an output came from. | A learned mapping may be harder to interpret; the 2023 survey identifies interpretability as a challenge. | Explicit components may aid inspection, but learned portions can remain difficult to explain. |
| How can errors propagate? | An upstream estimate can affect later prediction, planning, or control; modularity makes stages visible but does not eliminate errors. | Jointly learned features may connect perception and planning, but robustness and causal confusion remain concerns identified by the 2023 survey. | Interactions between learned and structured components create their own integration and failure cases to test. |
| What development evidence is needed? | Teams need to develop and evaluate the individual functions and their interfaces, then test the integrated system. | Training and evaluation depend on data and learning method; CARLA’s paper compares imitation-learning and reinforcement-learning examples, not a universal recipe. | Teams must evaluate learned components, explicit logic, and the behavior of their interfaces together. |
| What sensors and compute are required? | The architecture label does not specify sensor modalities or a compute budget. | The architecture label does not specify sensor modalities or a compute budget. | The architecture label does not specify sensor modalities or a compute budget. |
| Where may it operate, and who responds at a limit? | Not determined by the architecture label. The operating domain and fallback responsibility depend on the system and its automation level. | Not determined by the architecture label. The operating domain and fallback responsibility depend on the system and its automation level. | Not determined by the architecture label. The operating domain and fallback responsibility depend on the system and its automation level. |
| How is safety established? | Component inspection can help debugging, but safety claims require evidence about the integrated vehicle and its operation. | Model performance alone does not establish safety; unusual scenarios and system-level behavior still need evaluation. | Combining methods does not remove the need to validate interactions, fallback, and operations. |
Sensor choice cuts across all three architectures
Software architecture and sensor suite are separate design choices. A modular, end-to-end, or hybrid system can use sensor data in different ways; the architecture label does not tell you whether a vehicle relies on cameras, lidar, radar, or a combination.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsIn an October 2021 description of its own system, Waymo said it combines lidar, cameras, and radar, with machine learning used in perception, behavior prediction, and planning. Waymo described lidar as providing depth and 3D shape, cameras as supplying visual features such as traffic-signal color, and radar as useful for motion and in difficult weather. This is the company’s account of its system, not an independent comparison of sensor performance or a current industry-wide specification.
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In the same 2021 post, Waymo reported that its major perception, behavior-prediction, and planning software used machine-learning models benefiting from more than 20 million autonomously driven miles. That is a company-reported figure from October 2021, not a current or independently verified measure of comparative performance.
Why architecture is only part of safety validation
A vehicle’s safety depends on more than its model design: sensors and other hardware, system behavior, fallback arrangements, operational limits, testing, and responses to field issues all matter. In a 2020 post describing its own framework, Waymo organized safety into hardware, behavioral, and operations layers. It described scenario-based simulation and closed-course testing, simulated deployments, fleet response, and field-safety processes. That is a company’s account of its approach, not a universal safety standard.
Waymo’s post stated: “There is currently no universally accepted approach for evaluating the safety of autonomous vehicles – despite the efforts of policymakers, researchers and companies building fully autonomous technologies.” The statement is from Waymo’s company-authored 2020 safety-framework post; it should not be read as a claim about the present status of every regulator or evaluation program.
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